Systems and methods for creating and evaluating content and predicting responses to content

ABSTRACT

A method of creating new content based on previously experienced content and content ratings, in which content attributes are obtained and analyzed against set-top box events collected as the content is experienced. Analysis includes determining effects of various content attributes on content ratings, content attribute order on content ratings, and determining a preferred content attribute set and content attribute presentation order. Also taught is a system and method of predicting future events given a proposed dataset, in which past events are monitored and correlated with a proposed dataset sharing at least one attribute with, and having a substantially similar structure to, the past events, and the results of the correlation are reported as a prediction of future events. The invention further includes a method of predicting behaviors of non-sampled demographic specifications based on sampled demographic specifications of a given level, in which past behavior is monitored and correlated with demographic characteristics.

REFERENCE TO RELATED APPLICATIONS

The present application is a divisional of U.S. patent application Ser.No. 09/759,640, filed Jan. 16, 2001 now U.S. Pat. No. 7,139,723, andclaims priority thereto and to U.S. Provisional Patent Application No.60/176,177, filed Jan. 13, 2000. The teachings of both U.S. patentapplication Ser. No. 09/759,640 and U.S. Provisional Patent ApplicationNo. 60/176,177 are incorporated herein in their entirety.

FIELD OF THE INVENTION

The present invention relates to the fields of data collection and dataanalysis. In particular, the present invention provides a system andmethod for privacy-secure data collection and correlation of such datawith data from other sources.

BACKGROUND OF THE INVENTION

Advertisers tend to group prospective customers into broad demographicand geographic categories, possibly due to limitations in currentlyavailable market research methods with respect to determination of theeffect of their advertisements. In addition, they use informationgleaned from data mining to mass-market products to groups ofprospective buyers. Unfortunately, the data searched during this datamining often contains low-validity information that is derived fromsmall sample populations.

Due to these inherent data validity problems, statistics generated bysuch data mining may not accurately reflect a given market. That is, thestatistics may not mean that all persons in a group will buy a product,but rather they imply that some person in a group may have a higherprobability of buying the product than someone in another categorizedgroup. For example, the data mining may show that more scuba equipmentcould be sold to 20-40 year-olds in Miami than to 50-80 year-olds inKansas City.

Based on this data, advertisers carefully select the television shows,magazines, billboards, or other media on or in which theiradvertisements run. In the case of television, advertisers traditionallygravitate toward programs that garner higher ratings for desiredaudiences and then select advertising slots within those shows.Advertisers purchase ratings data from market research organizations,who collect and analyze data on the viewing habits of individuals andthen publish the results.

Examples of such research organizations include A. C. Nielson andArbitron. Such companies typically monitor television-viewing habits ofa relatively small number of viewers through telephone polls,specialized set-top monitoring “Nielson” boxes, or viewer diaries. Theresults of these surveys are then extrapolated to the population atlarge.

As can be expected, extrapolation of small-population data to thepopulation at large is prone to many different limitations, withaccuracy perhaps the most notable. For example, if there were only 200persons over 65 years of age in a sample, their compiled viewingbehaviors may be purported to be representative of the viewing behaviorsof the 35 million people in the U.S. over 65 years of age.

Obviously, larger, more random sample populations are preferred oversmaller sample populations. This is true because a larger samplepopulation tends to reduce the impact of suspect behaviors. Such suspectbehavior might include distorted or inaccurate information provided inwritten television viewing logs, or intentionally leaving the television“on” to a certain channel to insure higher ratings for a desired showeven if the individual being sampled is not watching that show. If thebehavior of even one of the 200 persons in the previous example wassuspect, this may translate to errors in the predictions ofapproximately 175,000 people; if the sample population is increased to50,000 people, an individual whose behavior was suspect would translateinto prediction errors for only approximately 700 people. As advertiserscontinue to base their decisions on small-sample data, they arecontinuing to question whether their advertisements are reachingintended audiences.

While accuracy is certainly a big problem in the prior art, it is notthe only problem. Another limitation is the specificity with whichbehaviors may be inferred as they pertain to specific demographicgroups. For example, if only one of 200 sampled senior citizens is asingle Asian with no dependents and has an annual income over $100,000,making an inference based on this more specific group is likely to behighly inaccurate; in many cases the behaviors of an entire demographicsub-group are attributed to the sampled behavior of only one person.

Another factor contributing to the inaccuracy of prior art isreliability. Invasive sampling methods such as those described above cancause many problems, including determining how much of the data can betrusted. Sampled individuals may not to be willing to disclose, forexample, that they watch adult (e.g., X-rated) programming or othercontroversial programming. Without such information, all data generatedbecomes unreliable.

Still another problem is that even if the sample data can be trusted,the memory of a sampled individual or the ability of a sampledindividual to adhere to documented guidelines may not be accurate orcomplete. If a given individual is asked what they watched last week,the likelihood that the response may be correct and specific is likelyto be low. Often, low response rates or missing journal information areextrapolated according to previously collected data and rules determinedtherefrom. However, this extrapolation is built on data generatedthrough the inherently faulty means described above.

The invasive sampling techniques used in the prior art also suffer froman inherent flaw. Since these methods are invasive and participation isoptional, differences between the types of persons who may be willing tobe sampled and those that are not willing to be sampled may not beaccounted for in such techniques.

While the effects of some of the problems in the prior art can belimited by increasing the population sample size, population sample sizeincreases are typically cost prohibitive. The increased costs are theresult of several factors, including equipment purchase, installation,and repair; data collection and validation; and participantcompensation.

However, even when equipment and other costs are taken out ofconsideration and larger samples are collected, such an increase insampling size does not solve all of the problems in the prior art. Forexample, the prior art also faces a problem with data resolution. Mostmajor media research organizations consider data in an all-or-nothingfashion. For example, if a set of channels was watched during somesampling interval, only the channel that was watched the most, or theone watched at the time of the sample, would be counted, and it would berecorded as having been watched for the entire sampling interval(typically anywhere from 30 seconds to several hours). Although some inthe prior art have attempted to mitigate this effect by sampling morefrequently, there is always the possibility that changes occurringbetween samples will be missed. Thus, the use of data collection methodsemployed by the prior art tends to result in the generation ofmisleading or inaccurate viewing data.

Data collected by media research organizations and inferences resultingtherefrom face still another problem; one of substance. The fact thatoverlapping data is collected across different medium types (digital,written, verbal, etc.) makes the determination of common denominatorsdifficult, and thus renders objective statistical mining impossible.Inferences drawn from such data may only be lateral in nature, andcannot be readily mined for trends. For example, while the datacollected may support the conclusion that one show is more popular thananother, the particular reason why one is more popular than the othercannot be extracted from this data. Such methods may be barely capableof supporting the most general popularity-type conclusions; any furtheranalysis upon relationships of the conclusions is likely to bequestionable at best, and accuracy may be lost each time morecomplicated, or deeper, inferences are drawn.

Unfortunately, there are many other problems with existing marketresearch methodologies, such as the use of “Sweeps” or ratings periods,but most of these problems are at least partiallystatistically-correctable. However, the five major issuesdiscussed—accuracy, group specificity, reliability, resolution, and datasubstance—are inherent to actively monitoring data within small samplesand cannot be overcome by the prior art.

SUMMARY OF THE INVENTION

The data collection techniques used in the prior art arise from a modeldeveloped in the 1960's and 1970's. At that time, market research datacollection and data transmission costs were very high, and a system ofperiodic sampling was established. A thirty second sampling window waschosen because a given household had an average of three channelsavailable, and “surfing” was a non-existent phenomenon; thus, it was asafe assumption that the same channel was watched for the entiresampling period. Today, most television viewers have over 65 channelsavailable to them, and they are barraged with more commercials per houron each channel. This combination gives viewers incentive to frequentlychange channels within what would be a sampling period in the prior art.

Obviously, sampling methods from the 1970's are not capable ofaccurately representing television viewing habits in the year 2000 andbeyond. Thus, a need exists to more accurately sample viewer data,insure the data collected is not suspect, infer from such datarelationships and trends in viewing habits, project sampled data moreaccurately to the population at large, and determine not only whatshows, advertisements, or other content were watched, but also but whatportions of such content were watched. There also exists a need forratings systems which can more accurately and objectively provideratings of future programs. The data collection and data analysisaspects of the present invention can readily fulfill these needs.

A preferred embodiment of the present invention can provide advertiserswith accurate ratings predictions of commercials and programs forspecific demographic groups, rather than just providing overall ratingsof programs which have already aired. While a preferred embodiment ofthe present invention involves television viewership statistics, thepresent invention can draw correlations between any datasetcombinations, such as, but not limited to, television program orcommercial viewership and sales figures, or sales figures anddemographics. The present invention may provide advertisers with abetter understanding of both consumer needs and their own advertisingneeds.

One aspect of a preferred embodiment of the present invention provides asystem and method by which viewing behaviors of television viewers canbe extracted electronically. While instrumentation and infrastructuredevelopment costs may be initially high, the present invention can allowdata collection from a vast number of households without significantdata collection, data storage, and data analysis cost increases as thenumber of households increases, or as the number of times data iscollected per household is increased.

The present invention takes a different approach to television marketdata collection than the prior art. Rather than periodically samplinguser behavior, the present invention tracks user behaviors by recordingset-top box events. Such a set-top box may record events including, butnot limited to, set-top box state changes, such as a set-top box beingturned on or off, channel changes, volume changes, the use of an SAPfeature, or muting of particular content; the use of interactive contentguides; Internet web site usage; and combinations of such events.Recorded set-top box events data may be periodically transmitted to acentral data collection point where data analysis may begin, or suchtransmission may occur instantaneously. In a preferred embodiment, thisdata collection method allows data to be gathered without requiringsubjects to keep journals, push buttons, or even know their behaviorsare being observed. This can be seen as an improvement over the priorart, as the invasive data collection methods used therein are likely todestroy data integrity.

The present invention also includes a method for mining compatibledatasets so that correlations and trends within and between the datasetscan be uncovered. The present invention is tailored to the analysis ofdatasets that are extremely large; result from passive, privacy-securedata collection; and are relatively unbiased, such as datasets collectedby set-top boxes described above. While the analysis of televisionmarketing data is presently preferred, it will be apparent to oneskilled in the art that the system and method herein can be employed inother data collection and data analysis scenarios. Other contemplatedembodiments include, but are not limited to, privacy-secure actuarialanalysis, radio and Internet market data collection, and thought andbehavioral predictions for artificial intelligence efforts andgovernmental planning.

The data mining and prediction portions of the present invention attemptto uncover the interrelated rules that cause various data to arise, anda preferred embodiment does so while still respecting privacy concerns.Privacy can be maintained through anonymous data collection, which canbe accomplished through a software upgrade to a standard set-top box. Ina preferred embodiment, a satellite, cable, or other television provider(“cable company”) can provide a viewer with a set-top box which may bespecially instrumented to allow monitoring, recording, and transmissionof set-top box events, as described above.

Traditional set-top boxes include a unique identification number (“ID”),and this number can be used by a preferred embodiment of the presentinvention for identification purposes in lieu of personal information.To facilitate data analysis, a cable company can provide to the presentinvention a geographically associated code, such as, but not limited to,a zip code or telephone number prefix, that corresponds with eachset-top box. Given this information, set-top box ID's to be monitoredcan be chosen through various means, including, but not limited to, thepresent invention selecting set-top box ID's at random, the presentinvention selecting set-top box ID's based on geographic coverage, acable company selecting ID's based on its own criteria, or selecting allset-top box ID's. A combination of set-top box ID and geographicallyassociated codes allows the present invention to maintain participantprivacy while still allowing for determination of detailed demographicinformation through the inverse mathematical methods described herein.

Although privacy is an important part of the present invention, analternative embodiment would allow set-top box operators to request alist of their viewing habits. This might be useful for parents orbusinesses wishing to monitor programs watched by their children oremployees during a given day, or parents or businesses wishing tomonitor other DATA1 datasets, such as internet viewing behaviorsexhibited by employees or family members.

Analysis of data collected through a privacy-oriented approach such asthe set-top box method described above is inherently self-limiting, asonly viewership information for a particular show or commercial can bedetermined for a given time over the sample population. While this maybe of interest to advertisers, an advertiser's real concern is that ashow is reaching a particular target-market, and thus that they arespending their advertising money on shows which users of their productswill watch. Thus, advertisers prefer detailed, grouped market researchdata, such as the ages, incomes, and other demographic informationassociated with a show's viewers. Through its novel data-correlationscheme, the preferred embodiment of the present invention can determinesuch information while still maintaining the anonymity of those beingsampled. However, the present invention is not limited to providing onlycorrelations between user behavior and demographic data; the presentinvention can draw correlations within and between any number of datasets with a common feature, such as the zip code of a given televisionviewer and the demographics associated with that zip code, regardless ofthe data represented therein.

In a preferred embodiment, the present invention augments televisionviewing behavior data collected from set-top boxes with relativelystatic data from outside sources, such as, but not limited to,demographic information from demographic providers, news informationfrom news providers, weather information from weather providers, andsales information from advertisers, manufacturers and producers. Thisinformation can be used not only to increase the number of categoriesinto which individuals may be grouped, but also to take into accountspecific confounding events, such as a severe weather alert, a nationalor regional news story, a local school's play-off game, and specialpromotional offers. Demographic, regional, and other such relativelystatic data may be updated at intervals specific to the type of datacollected.

The present invention may make certain assumptions based on collecteddata to reduce data storage requirements. These assumptions arewell-fitted with the use of matrix manipulation schemes. For example,the present invention may assume individual age is intrinsic to aperson, household income is intrinsic to a household, and weatherpatterns are intrinsic to geographic regions. Thus, demographicinformation comprising a matrix needs not be mutually exclusive. Thatis, if weather is a considered factor, weather data need not becollected and stored for every person in a geographic region, but simplycan be held in one matrix that can be accessed for all people livingwithin that geographic region.

The present invention can draw inferences within and between data setsthrough a variety of means. In a preferred embodiment, the presentinvention may use inverse mathematical methods to perform the desiredanalyses. These methods can be more simply expressed as techniques oflinear and matrix algebra.

With established data mining and data comparison methods in place,another step is to extrapolate any calculations to the rest of thecountry. Through the data-collection methods described above, presentinvention can track viewing behaviors, demographic characteristics, andpreferences associated with a geographically based group of people. Ifthis same demographic information is obtained for the country as awhole, the present invention can use the data stored therein toextrapolate out its results to any region of the country, or to thecountry as a whole. The present invention includes, but is not limitedto, the development of an extrapolation system, which itself involvesthe use of mined trends and will evolve and improve over time. Forexample, “white males age 70-80” in the non-sample population may beconsidered to have more similarities with all persons age 70-80 in thesample than with whites alone due to a dominance of the age factor inwhites. Due to characteristics particular to a given geographiclocation, such as, but not limited to, such a location including aresort community, or a local sports team in a playoff game, somefactors, such as age, race, and gender, or even entire geographicregions, may be ignored in extrapolation procedures.

While some in the prior art have attempted to provide statistics similarto those available through the present invention, none have done so atconfidence levels approaching those provided by the present invention.In addition, the present invention improves over the prior art byallowing the extrapolation of very specific cases, even when no dataexists for that specific case. For example, if no Single Asian Males,23-24 years old, with partial custody of 1 child, one previous marriage,with a B.S. in Chemistry, working as an assistant in a ChemicalLaboratory, having an income between $24,000-$27,000 per year, andliving in a specific zip code in Miami, Fla. were in the sample, thepresent invention could still calculate an anticipated behavior basedupon combinations of subsets of these characteristics and their observedinfluences.

In fact, not only can the present invention infer viewing preferencesand other behaviors for previously aired content, but, as additionalsample data is collected, the present invention can also predictreactions to future content. The present invention can characterizepreviously aired content, such as a television program or a commercial,based on specific attributes thereof, such as volume changes; colorchanges; changes in brightness or contrast; speed of motion; backgroundmusic mood; content; genres; actors appearing on the screen; plotlines;languages spoken; use of foul or offensive language; and the like. Thisinformation can then be cross-referenced against viewer reaction to thatcontent, and suggestions can be made to make the content more appealingto a particular audience. With a database of viewer reactions topreviously aired content, the present invention can also be used toanalyze proposed content before it is aired, or even to suggest optimalprogramming content structure and substance.

It should be obvious to one skilled in the art that the system andmethod described in this specification are not constrained by the samelimitations as traditional data collection and analysis techniques. Thepresent invention provides non-invasive sample data collection,significantly increasing reliability. The viewing habits of increasinglyspecific demographic groups can be ascertained while still maintaininghigh accuracy levels.

Additionally, the behavior data resolution is so fine as to allow theredefinition of television viewing behavior. For example, the exactpercentage of each program that each group watched can be determined. Inaddition, the answer to “What percentage of Group A watched at least 80%of Program 1 who watched less than 10% of Program 2 three weeks ago?” isjust as easy for the computers to determine as a seemingly simplerquestion. Furthermore, event data for every set-top box and geographicregion can be archived as a large, unbiased database of 1's and 0's;therefore mining the data for trends would entail literally 0% loss ofrelated-accuracy. This is to say that the data's substance is the sameregardless of specificity of analysis.

It should also be obvious to one skilled in the art that the system andmethod described above can be used not only to rate television programs,but, unlike the prior art, the present invention can also ratetelevision advertisements. The prior art is limited to televisionprogram ratings because the sampling periods required to accurately ratetelevision advertisements would result in more data than can beaccurately collected, handled and characterized by the prior art. Thenovel data collection method described above provides viewer behaviordata at a finer resolution than is possible through the sampling methodsimplemented by the prior art, and can thus be used to determine viewerbehavior at any instant, including sections during a commercial.

Once data is collected, a further aspect of the present inventionprovides advertisers or others wishing to analyze the data with aninteractive interface for such data analysis. Such an interface canallow data analysis requests to be entered through a variety ofinterfaces, such as through command-line queries, graphical interfaces,or even as natural-language questions. Further, an output representationmay be selected, including, but not limited to, raw data, pie chart,time-progression, and the like. The present invention may further trackfrequently requested analyses and automatically update those on aperiodic basis to expedite delivery of such information.

While the present invention improves over the prior art when evaluatedusing current advertising schemes, the present invention can also allowa new type of advertising. Rather than an advertiser purchasing timeduring a given show that is broadcast to a large audience, many of whommay not be in a product's target audience, the present invention mayallow advertisements to be delivered to only those set-top boxes whoseviewers exhibit certain behaviors or exhibit a propensity towardspecific products or services. This allows advertisers to directly reachthose viewers who would be interested in an advertiser's product orservice, thus decreasing the cost per viewer of running suchadvertisements. A very simple example of such is continuing to showbicycle-related commercials to those who haven't turned the channel whenbicycles have been shown in the past, or have recently bought orsearched online for a bicycle.

Thus, it can be seen that the present invention represents significantimprovements over the prior art. Not only can the present inventioncollect more reliable data through its use of private, non-invasive datacollection techniques, but the present invention can also provide datawhich lends itself to more advanced, thorough, and privacy-secureanalysis techniques. The present invention can also analyze data moreaccurately than data analysis and data mining techniques of the priorart. Further, the present invention allows data analyses to be performedon behaviors observed from a larger portion of the population than theprior art, and can more accurately extrapolate such data to thepopulation in general.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram providing a general overview of the consumerdata acquisition, prediction, and query system of the present invention.

FIG. 2 is a block diagram of the market data acquisition, prediction,and query system of the present invention.

FIG. 3 is a block diagram of a Tuner Data Collection System of thepresent invention.

FIG. 4 is a block diagram of a Past Events Query System of the presentinvention.

FIG. 5 is a block diagram of a Graphic System of the present invention.

FIG. 6 is a block diagram of the Individual Behavior DeterminationSystem of the present invention.

FIG. 7 is a block diagram of the Future Events Query System of thepresent invention.

FIG. 8 is a block diagram of the Program Entry and Program Buildersystems of the present invention.

FIG. 9 is a block diagram of the Data Mining and Prediction System ofthe present invention.

FIG. 10 is a block diagram illustrating aspects of a sample IDMCalculation Algorithm of FIGS. 2, 4, 5, 6 and 9.

FIG. 11 provides a high-level view of a technology infrastructureemployed in a preferred embodiment of the present invention.

FIG. 12 is a graph of linear equation values and weights for a givengeographic region.

FIG. 13 is an additional graph of linear equation values and weights fora given geographic region.

FIG. 14 is an alternative view of the graph of FIG. 13.

FIG. 15 is an alternative view of the graph of FIG. 14, and includesadditional details.

FIG. 16 is a sample, single-peak graph of linear equation values andweights for a given geographic region.

FIG. 17 is a sample, two-peak graph of linear equation values andweights for a given geographic region.

FIG. 18 is a sample, random pattern graph of linear equation values andweights for a given geographic region.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

FIG. 1 is a block diagram providing a general overview of consumer dataacquisition, prediction, and query systems of a preferred embodiment ofthe present invention and their interaction with each other. In thisembodiment, the present invention may monitor user behavior while a userexperiences television, radio, Internet, or other content. Examples ofsuch content can include television shows, radio shows, music,advertisements, news, weather, and other multimedia orsensory-stimulating material.

Airings Data 110 comprises detailed content attributes. Examples of suchcontent attributes include times at which such content was available;geographic or other regions to which such content was made available;actors or models appearing in or otherwise associated with such content;types of characters portrayed by such actors or models; content authors,producers, and directors; content genres, subjects, and settings;background music tones, tempo, and related characteristics; visualeffect speed, colors, pixel change ratio, brightness, and other relatedcharacteristics; scents and tastes associated with such content; andother such content attributes. Additional content attributes stored byAirings Data 110 may include general plot themes or plot styles, such ascomedic or dramatic segments; and a time- or position-based order inwhich such content attributes appear within such content.

As users view, listen to, or otherwise experience content, user behaviormay be monitored through a set-top box, personal computer, radio,portable music player, or other device (“set-top box”). In a preferredembodiment, user behavior may be monitored by recording set-top boxevents. Tuner Data 120 may comprise a collection of such user behaviorinformation. Tuner Data 120 may also comprise other user information,such as, but not limited to, billing information and personaldemographic information.

Airings Data 110 and Tuner Data 120 may be transmitted to a data centerof the present invention via a telecommunications infrastructure. In apreferred embodiment, such telecommunications infrastructure may includecable television systems, satellite television systems, telephonesystems, or other wired or wireless telecommunications systems.

In addition to the above-described information, the present inventionmay include data from external sources, as indicated in FIG. 1 byGraphic Data 140. Graphic Data 140, also referred to as DATA2, mayinclude demographic, geographic, sales, weather, and other information(“demographic information”). Geographic information used by the presentinvention may include, but is not limited to, distances between zipcodes, population sizes within a zip code, and terrain types (coastaltown, metropolitan area, etc.) within a zip code. Demographicinformation used by the present invention may include, but is notlimited to, age, race, gender, and income distributions within a zipcode or sub-zip code. In a preferred embodiment, such data may be ofhigh enough resolution as to provide information at the zip code orsub-zip code level. The only requirement is that such data share acomment aspect, such as zip code, with Tuner Data 120 or any other DATA1data with which it may be correlated.

Data Center 130 represents a database or other data storage device inwhich data from Airings Data 110, Tuner Data 120, Graphic Data 140, andthe like can be stored. Data Center 130 may correlate data storedtherein (such as correlating Airings Data 110 to Tuner Data 120), andsuch correlations may indicate content that was viewed, listened to, orotherwise experienced (“viewed”) by a user or group of users, and anyreactions thereto.

In a preferred embodiment, Algorithms 150 may use one or morestatistical methods to determine correlations among and between datastored in Data Center 130. Such correlations may include, for example,which persons and groups viewed certain content, and why. Past EventsQuery System 200 may allow users to extract meaningful and directedinformation from Data Center 130 using Algorithms 150 as describedabove. Past Events Query System 200 may focus on extraction ofprobabilities for events that have already taken place. These events caninclude, but are not limited to, past viewing habits of demographicgroups, past sales based on advertising, past sales and viewing based onweather, and the like.

In a preferred embodiment, Past Events Query System 200 may comprise aweb-based system that allows customers to query a database of pastviewing behaviors. A web-based system may utilize a natural language,graphical, or command-line input interface for such queries. The PastEvents Query System 200 may allow a customer to query Data Center 130while preventing a customer from obtaining any information aboutindividual consumer behaviors, or allowing a customer to duplicateprocesses employed in Algorithms 150 to produce such information.

As used in this specification, the term query can include anydata-related question asked of the present invention. A query mayconcern specific content, content portions, content combinations, ormixtures thereof. By way of example, without intending to limit thepresent invention, a query may be, “How many African Americans inFlorida, but not in Bradenton, watched at least 20% but no more than 80%of the primetime Friends last week, but did not watch at least 45% ofthe rerun of Seinfeld right before it or at the same time 3 weeks beforethat.”

While Past Events Query System 200 can generate statistics fortelevision airings or other events that happened in the past, thepresent invention is not limited to such queries. Customers, illustratedby Block 210, can also enter predictive queries through Future EventsQuery System 190. Future Events Query System 190 can, in turn, parsesuch requests into terms useable by Prediction System 180.

Prediction System 180 may predict future viewer behavior based upontrends found in Data Center 130 and extracted using algorithms inAlgorithms 150. While these trends are of limited predictive use, suchtrends can be analyzed against demographic data specific to eachmonitored set-top box, thus providing better analysis of viewing trendsacross various demographic groups. Such analysis can be performed byIndividual Behavior Determination System 160. The addition of FutureAirings Data 170 allows further predictive refinement, as Future AiringsData 170 provides a basis onto which behaviors can be mapped orextrapolated.

Customers 210 of the system may access data that has been analyzed byAlgorithms 150 through Past Events Query System 200 and Future EventsQuery System 190. By these systems, a customer may tailor queries sothat Algorithms 150 or Prediction System 180 may answer them. Due topossible privacy concerns, Customers 210 may not have direct access toIndividual Behavior Determination System 160, thus restricting access tobehaviors of sampled households or persons.

FIG. 11 provides a high-level view of a technology infrastructureemployed in a preferred embodiment of the present invention. Asillustrated by FIG. 11, analog or digital set-top boxes (Blocks 1100 to1102) reside in viewer's homes, and can control content presentation.Such control can include, but is not limited to, the selection of atelevision channel or increasing or decreasing volume. Such set-topboxes may also include software which provides additional set-top boxfunctionality, such as, but not limited to, managing communicationsbetween a set-top box and a head-end (Block 1103), monitoring set-topbox events, forwarding events to a head-end, and managing bandwidthutilization via configurable application parameters.

A head-end bunker can house equipment that distributes contentdownstream to a group of households. In a preferred embodiment, ahead-end bunker can also include a combination of hardware and softwarethat monitors user behavior information from downstream set-top boxes(Block 1105). In a preferred embodiment, such set-top box data can betransmitted to a head end through a cable television cable, telephoneline, or other telecommunications infrastructure. Such transmissions canalso occur through a cable shared with return path equipment, eventhough such equipment may be separate from distribution equipment.

In a preferred embodiment, a head-end bunker, as used in the prior art,may be enhanced with the addition of a UNIX-based server (Block 1106)that is connected to return path equipment via a telecommunicationsinfrastructure. Such a server may allow collection of user behaviorinformation.

A preferred embodiment of the present invention also provides a serverwith access to information from a customer billing database (Block1104). Such billing system access can provide correlations betweenset-top boxes and customer data, such as billing zip code, billing areacode and prefix, and the like. To address privacy issues regardingviewership, a preferred embodiment of the present invention willidentify set-top box data by zip code, area code and prefix, or othergeographic identifier associated with a region in which a set-top boxresides. Correlations between set-top boxes and zip codes can bemaintained in a cable television or other content provider's billingsystem; thus, access to such billing data may be preferred.

A highly available and highly reliable server is preferred for set-topbox event monitoring, as such a devices may reside in a rack at ahead-end bunker, and head-end bunkers may be physically disparate or inremote regions. A preferred embodiment of Server 1106 includes aUNIX-based server; a UNIX-based server is preferred as such servers mayreduce maintenance requirements. In addition, backup circuits may beimplemented to provide fault tolerance depending on availabilityrequirements for gathered data.

Server 1106 can also attach to a network access device (Block 1107) toupload data gathered from set-top boxes to a data center. Such networkaccess devices can include, but are not limited to, modems, routers, andsatellite transceivers. As illustrated in FIG. 11, a private networklink (Block 1108) is preferred for connecting a server to a data centerfor data uploads, as well as network and systems management, and forother functions. However, such functions may also be accomplished acrossa shared network, such as the Internet. Data transmitted across publicor private networks may be encrypted or otherwise encoded to reduce thelikelihood that such data may be used by unauthorized individuals.

Data uploads may occur in real-time or data may be temporarily stored ona server and transmitted to a data center on a periodic basis. Suchperiods may be time based, or may be based on the occurrence of anevent, such as, but not limited to, receipt of a certain quantity ofdata or data from a particular set-top box.

In a preferred embodiment, data transmitted by Server 1106 may bereceived at a data center. Such a data center may be a centralrepository for all data gathered from a plurality of head-ends. FIG. 11includes an illustration of major data center components.

Data from Server 1106 may come into a data center through wide areacircuits (Block 1109) and into temporary storage space (Block 1110). Anydata cleansing or pre-processing prior to import of such data into themain database can be accomplished as data is stored thereon.Pre-processed data may then be imported into a main data store (Block1114).

In addition to user behavior data, a data center's main database mayalso store or access data from sources external to the presentinvention. Such external information may include, but is not limited to,content attributes from various content providers (Block 1111),demographic information from third party providers (Block 1112), andsales data from retailers or producers.

As illustrated in FIG. 11, data stored in a main data store may also bereplicated to one or more databases for other purposes. In a preferredembodiment, data may be replicated to a database that is dedicated toInternet access (Block 1113), and another database that is dedicated toreport generation (Block 1115). Such replication may provide datasecurity, as data stored in one database can be compared against datastored in other databases to ensure its authenticity. Data storage andretention properties can also be adjusted for each server as needed.

By way of example, without intending to limit the present invention,Internet Database 1113 can be configured to provide dedicated access toa time-limited amount of viewership data. A web-based application (Block1117) can then provide customers with access to data in InternetDatabase 1113, and can also analyze and report on such data. Web servers(Blocks 1121 through 1123) can provide a front-end query system forcustomizing such analyses and viewing reports. In a preferredembodiment, fields and data made available through Internet Database1113 will also be structured to ensure that queries complete in areasonable period and that impact on other users is controlled.

As an alternative example, again without intending to limit the presentinvention, data replicated to a reporting database (Block 1116) can beused to create hard copy reports (Block 1118), electronic reports (Block1119), and CD-ROM's (Block 1120) for customers who request access todata by means other than through a Web interface. A reporting databasemay also have time-limited data retention.

FIG. 2 is a detailed block diagram of market data acquisition,prediction, and query systems of a preferred embodiment of the presentinvention. Although FIG. 2 includes language specific to this preferredembodiment, the principles of the present invention are also illustratedthere, and can be seen with respect to any arbitrary data by replacing‘Tuner Data Center’ by ‘DATA1’, ‘Interval Updating Graphic Database’ by‘DATA2’, and ‘Sales Data’ by ‘DATA3’.

While a preferred embodiment of the present invention applies theconcepts of the present invention to a television ratings system, thepresent invention has other applications as well. Such applicationsinclude, but are not limited to, Internet advertising and actuarialanalysis in the insurance industry. Whenever multiple datasets exist andcorrelations are desired between such datasets, the present inventioncan draw such correlations provided at least one dataset is relativelystatic and a common aspect, such as a zip code, is shared between thedatasets. In the preferred embodiment disclosed in this specification,DATA1 can represent a variable data set, such as data from a set-topbox, and DATA2 may represent a relatively static dataset, such asdemographic data for a given geographic region. The system and methoddescribed herein can determine correlations between such datasetswithout direct knowledge of DATA2 values for DATA1 data.

FIGS. 3 through 9 illustrate individual modules of the presentinvention, and FIG. 2 illustrates the modules of FIGS. 3 through 9overlaid atop one another and interconnected, thereby illustratinginteroperability of various modules and relationships between suchmodules. Elements of FIG. 2 will be described below in connection withFIGS. 3 through 9.

FIG. 3 is a block diagram of a Tuner Data Collection component of thepresent invention. In the more general functionality provided by thepresent invention, FIG. 3 illustrates the acquisition of some data,DATA1. As it relates to a preferred embodiment, FIG. 3 is a flow chartof modules useful in moving tuner data to Data Center 130 of FIG. 1.

Set-Top Boxes 310 can comprise one or more set-top boxes, which can belocated in one or more households. Set-Top Boxes 310 may collect andrecord event information based on behavior of one or more sampled users,as well as embedded content attributes, where such attributes areavailable. Such embedded content attributes can allow the presentinvention to quickly match data about specific content to set-top boxevents, rather than pulling such attributes from external data sources.Embedded content attributes may pertain to content with which saidattributes are transmitted, or embedded content attributes may pertainto previously presented content or content to be made available in thefuture.

Set-Top Boxes 310 may constantly transmit state-change information toHeadEnd Bunker 320, or Set-Top Boxes 310 may send batches ofstate-change information to HeadEnd Bunker 320. HeadEnd Bunker 320 canforward such state-change information, along with content attributes(Block 330), to Sorting System 900.

Sorting System 900 may comprise one or more sorting algorithms thatplace set-top box event data into efficient arrays. Due to reliabilityissues associated with data from set-top boxes operated by a consumerwho knows he or she is being observed, these sorting algorithms mayseparate data into two or more classes based on whether a set-top boxowner or operator has specifically requested access to monitored data,or is otherwise aware that they may be monitored.

In a preferred embodiment, the present invention may not collectindividual-specific information, such as name, size of family, name ortype of business, address, and the like from sampled users, with theexception of a zip code, area code and prefix, or other geographicidentifier. Local Cable Provider 340 or other entity, which may beacting as a privacy guard for a sampled population or a governmentalagency, may also provide these geographic identifiers.

While data acquired by HeadEnd Bunker 320 may contain embedded contentattributes, not all content may be so encoded. Non-embedded ProgramInformation 350 may be acquired from a content airing source, such asLocal Cable Provider 340, or possibly other sources, such asInternet-based guides. Non-embedded Program Information 350 may compriseinformation that identifies content for which attributes are notavailable. Where such data is not electronically available, employeesmay make phone calls, consult published guides, or otherwise obtain suchdata through manual methods.

These latter methods and data collected thereby are illustrated in FIG.3 as Airings Source 360. Airings Source 360 may also include a list ofcontent that may be available at a time in the future. In addition,Production Team 370 may work with content creators to provide content toLocal Cable Provider 340 and provide Non-Embedded Program Information350 to Airings Source 360. Production Team 370 can include anorganization working with content creators who have access to programdetails and airings times for Non-Embedded Program Information 350. Inits representation in the figures, Production Team 370 may refer to anyunit or process involved in content creation or distribution, such aswriters, producers, studios, networks, and the like.

As Sorting System 900 receives such data, appropriate sorting may occurand correlations may be drawn between such data and data from Block 330.Sorted data may be stored in Tuner Data Center 930. Tuner Data Center930 may comprise a database of set-top box data arrays of relevant age.Arrays of information of non-relevant age may be stored off-line, butmay still be permanently accessible.

FIG. 4 is a block diagram of Past Events Query System 200 of FIG. 1.Although illustrative of a preferred embodiment of the presentinvention, FIG. 4 also illustrates the general concepts of the presentinvention with respect to any arbitrary DATA1 and DATA2 if ‘Tuner DataCenter 930’ is replaced by ‘DATA1’ and ‘Interval Updating GraphicDatabase 620’ is replaced by ‘DATA2.’

Graphic Vendor 610 may comprise one or more data vendors supplying thepresent invention with demographic data for a geographic or otherregion. Graphic Vendor 610 may provide such information broken intodistribution units, where such distribution units share a common factorsuch as zip code or sub-zip code.

While Graphic Vendor 610 may supply such data in a preferred embodiment,an alternative embodiment can replace data supplied by Graphic Vendor610 with data determined internally by a Graphic System as illustratedby FIG. 5. A Graphic System can use data collected by an IndividualBehavior System, which is illustrated in FIG. 6, to provide necessarydata. In another embodiment, data from a Graphic System can be augmentedby data from Graphic Vendor 610 to provide the present invention withmore comprehensive data.

In a preferred embodiment, the present invention may periodicallyrequest data from Graphic Vendor 610 and such data can be stored inInterval-Updating Graphic Database 620. As Interval-Updating GraphicDatabase 620 receives data from Graphic Vendor 610, data stored inInterval-Updating Graphic Database 620 may be modified to reflectchanges implied by data from Graphic Vendor 610. Interval-UpdatingGraphic Database 620 may then be used by the present invention as asource of graphic data.

The present invention may use data from Interval-Updating GraphicDatabase 620 to create data arrays representing relative datadistribution percentages. Such arrays can be compiled by IDGM GraphicMatrix 910 at any time. In a preferred embodiment, IDGM Graphic Matrix910 may be updated when new data is received by Interval-UpdatingGraphic Database 620.

IDGM Graphic Matrix 910 may create matrices for each set of graphicdata. Such matrices may contain arrays that refer to zip codes or othergeographic descriptors to which information contained within an arraycorresponds. In a preferred embodiment, arrays may be formed with columnheadings corresponding to graphic characteristics, such as, but notlimited to, gender or age, and rows corresponding to a set of zip codes.A number corresponding to the percentage of said row that can beattributed to said column may be stored in the intersection of each rowand column. Thus, for example, if 65 percent of the population of aparticular zip code were male, 0.65 could be stored in the intersectionof the male column and the row corresponding to said zip code. Sucharrays can then be used by a Process Computer for matrix operations thatprovide numerical data to a report processor prior to delivery to acustomer.

Customers, illustrated in FIG. 4 by Market Customer 530, may requestsuch reports from Past Events Query System 200. The present inventionmay translate such a request into a mathematical formula, or amachine-language representation of such, through Post-Translation System280. Formulae created by Post-Translation System 280 may be interpretedby IDGM Calculation Algorithm 270 to properly extract and analyze datastored in IDGM Graphic Matrix 910 and Interval-Updating Graphic Database620.

FIG. 10 provides an overview of a sample IDGM Calculation Algorithm 270that can perform such analyses. As illustrated by Block 1012, algorithmsused by IDGM Calculation Algorithm 270 may take advantage of anassumption used by media researchers, which is that each member of agiven DATA2 (Block 1011) group has the same probability of exhibitingsome DATA1 (Block 1010) behavior as any other member of the same group.The present invention extrapolates from this an assumption thatprobabilities associated with behaviors of groups of people can bedetermined by their demographic specification (this is referred to asthe “demographic assumption”). IDGM Calculation Algorithm 270 usescalculations derived from these assumptions to develop DATA2correlations for DATA1 data without collecting DATA2 information aboutDATA1 data directly, and to determine confidence intervals associatedwith such correlations.

IDGM Calculation Algorithm 270 may use inverse mathematical principlesto find such correlations (Blocks 1013 and 1014). The following are twomethods through which such correlations may be determined by IDGMCalculation Algorithm 270. While these examples are provided forenablement and best mode purposes, these examples should not beconstrued as limiting the present invention. In alternative embodiments,subsets of these examples may be used, as may additional calculationmethods.

A preferred embodiment of the present invention assumes that a person'sdemographic description has some influence on his choice of televisionviewing. A goal of this embodiment of IDGM Calculation Algorithm 270 isto apply the inverse of this assumption; that is, a person'sdemographics can be determined from his viewing habits. To achieve this,the present invention can invert region-specific viewing and demographicdata to compute demographic-specific viewing information.

The following are definitions that will aid in understanding thisembodiment:

Demographic Data—As with this specification as a whole, the term“demographic data” includes ordinary demographic categories as well asgeographic variables and general local characteristics. Examples ofordinary demographic categories include, but are not limited to, age,race, gender, income, education level, marital status, and number ofdependents. Examples of geographic variables include, but are notlimited to, climate and weather, urban or rural environment, coastal orinland geography, population density, and amount of traffic. Generallocal characteristics may include, but are not limited to, progress ofregional sports teams and local news events.

Demographic Characterization—A demographic characterization is a set ofvalues for each of a given set of demographic categories.

Demographic Characterization Level—A demographic characterization levelis the number of categories comprising a demographic categorization. Forexample, a level-one characterization might be a specification of race,while a level-two characterization might be an age group together with arace.

Demographic Specification—A demographic specification is a fulldemographic characterization that uses all demographic categoriestracked by the present invention.

Demographic Aspect—Demographic aspects are potential demographiccategory values. For example, the category “gender” has aspects male andfemale.

Orthogonal Characterizations—A set of demographic characterizations issaid to be orthogonal if there is no overlap among them; that is, agiven person can fit into no more than one of them.

Complete Characterizations—A set of characterizations is said to becomplete if any given individual person necessarily falls in at leastone of the characterizations in the set.

Orthogonal and Complete Characterizations—A set of characterizations issaid to be orthogonal and complete if any given individual falls intoexactly one characterization.

STB—A set-top box.

Program State—Program states reflect particular content, or portions andcombinations of content, presented by an STB at a particular time. Forexample, a program state could be defined as some specific 10-secondinterval of a particular commercial which just aired, combined with anentire program that aired 2 weeks ago.

Tuner State—Tuner states represent a current STB state.

Event Rating—An event rating represents a probability that a person orgroup of people, represented by a demographic characterization, matchedor will match an STB event.

ERINRating—An erinRating is produced for each event rating by proratingall event ratings for a given geographic area and over a given timeperiod with a given set of program state choices. The number of personsin a geographic area exhibiting an event can be determined bymultiplying the number of persons matching a demographiccharacterization in a given area by an associated erinRating.

STB Event Time—STB event time is a time series that is defined by STBevent sampling. For example, if a cable provider's system is inoperablefor 10 hours, this gap is not considered in STB event time.

A goal of the present invention is to determine the extent to which thedemographic assumption is valid. This determination can be a factor incalculating confidence intervals for data resulting from the presentinvention.

The demographic assumption can be expressed mathematically in thefollowing relation equation:

$\begin{matrix}{m_{k} = {\sum\limits_{i}{p_{k\; i}v_{i}}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

Here m_(k) is an observed number of STB's in zip code k experiencingsome defined event, p_(ki) is the number of people in zip code k withdemographic characterization i, v_(i) is the fraction of people ofcharacterization i that are watching the event, and the sum is over acomplete set of demographic characterizations i. This formula embodiesthe demographic assumption because v_(i) depends only on i. In English,Equation 1 simply says that the total number of STB's experiencing aparticular event can be determined by summing the number of people ofeach demographic characterization that are experiencing the event.

In a more general application of the present invention and its relatedformulas, m_(k) can be seen as corresponding to DATA1, and p_(ki) toDATA2.

Given that m_(k) values can be determined through the present invention,and p_(k) values can be obtained from demographics vendors or by othermeans, the present invention can use Equation 1 to solve for v_(i). Thiscan be accomplished by defining an error functions such as:

$\begin{matrix}{\Psi^{2} = {\sum\limits_{k}{\sum\limits_{i}\left( {m_{k} - {p_{k\; i}v_{i}}} \right)^{2}}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

If a dataset under consideration contains more zip codes thandemographic characterizations, Equation 2 can be solved through astandard least-squares approach. If a dataset under considerationcontains fewer zip codes than demographic characterizations, fittingmethods may be applied prior to application of a standard least-squaresapproach. A least-squares approach can involve inverting a matrix basedon p_(ki), and for that reason is referred to as an inverse demographicmatrix, or IDM, solution. This is illustrated by Block 1014 in FIG. 10.

An IDM can be implemented in different manners, depending on the set ofdemographic characterizations. For example, if a particular queryinvolves only one category, such as age, then demographiccharacterizations can be defined across the whole set of age-intervals(0-10, 11-20, 21-30, . . . , 101-110, 111+, for example). In this case,a set of twelve age-intervals forms a complete (all individuals fallinto at least one interval), orthogonal (an individual falls into nomore than one interval) set of characterizations. A resulting p_(ki)matrix will then be a matrix of size N_(zip)×12, where N_(zip) is thenumber of zip codes used in the calculation. Equation 2 can then besolved for twelve values of v_(i), v₁ through v₁₂.

Alternatively, if a query involves only one particular age-interval,such as ages 11 to 20, a set of demographic characterizations with onlytwo elements can be used, one set containing people between 11 and 20,and one containing all others. The resulting set is also complete andorthogonal, and can be represented in a p_(ki) matrix. In this case, thep_(ki) matrix is N_(zip)×2, and Equation 2 can be solved for two valuesof v_(i), v₁ (the number of people in the age range 11 to 20 who arewatching), and v₂ (the number of people of all other ages who arewatching).

An IDM solution for a query involving a set of level-n characterizationsmay be referred to as IDMn. The age-group example given above, using anN_(zip)×12 p_(ki) matrix, can thus be seen as an example of IDM1. If aquery involves two categories, for example age group and gender, then acomplete, orthogonal, level-2 demographic characterization set wouldhave 24 elements, and an IDM2 solution would involve a p_(ki) matrix ofsize N_(zip)×24.

When a query involves only a single characterization, such as agesbetween 11 and 20, this may be referred to as IDMn-P. Thus, the secondexample in the previous paragraph is an IDM1-P solution. As anotherexample, if a query only involved women in age interval 21 to 30, thenan IDM2-P solution, for which the p_(ki) matrix would again be of sizeN_(zip)×2, could be used.

IDM solutions can be normalized with complementary IDMn-P solutions tosupplementary IDM(1, 2, 3 . . . n)-P solutions. Complementarynormalization involves IDMn-P computation of all characterizations ofthe same “n” which contribute to a whole IDM(n−1) mutually exclusivedemographic characterization. IDMn-P values can then be normalized tototal the value computed for the DM(n−1)-P characterization (which waspreviously normalized itself if n>1). All normalization can begin atcharacterizations of lowest n value. By way of example, when n equals 7,characterizations 1 through 6 should be calculated, so thatnormalization occurs between all two-level combinations.

While the present invention may calculate an IDM solution, such asolution may not be presented to a customer querying the presentinvention. Rather, the present invention may present a range of valuesthat fall within a particular level of statistical confidence. As STBusage expands to include the majority of the population, such valueranges may have a non-zero size due to a violation of the demographicassumption. Values for such ranges can be determined by uncertaintyestimations provided by a least-squares fit.

While STB's are well below complete penetration but data is sampled in arandom fashion, value ranges returned as a result of a query can have anadditional uncertainty contributed by sampling error. In addition,sample bias, which can occur, for example, when a sampled individualknows of such sampling, or simply as a result of a difference betweenthose willing to be sampled and those unwilling to be sampled, can causeadditional sampling error. The methods outlined below can address thesecomplicating issues, and can calculate error ranges for variousdatasets.

As data is collected by the present invention, a 2-dimensional arrayholding the number of matching events between all combinations ofdemographic characterizations is kept in a specification similaritymatrix, illustrated by Block 1026 in FIG. 10. For each demographiccharacterization combination, the Pearson-r correlation can be computedwith respect to all pre-defined events.

Over time, IDM values for demographic characterizations or combinationsof demographic characterizations can become predictors for demographiccharacterizations, within determinable statistical confidence intervals.Such demographic characterizations can be combined to create asimilarity index. The present invention can use a similarity index todetermine probability ranges for various levels of demographiccharacterizations that can be used by a possibilities reduction systemof the present invention.

The present invention may also apply another assumption to determine acombination of demographic aspects that can have a relationship ontelevision viewing. Such relationships can be determined by applyingrules to demographic aspects over time. Such rules may include, but arenot limited to, additive, subtractive, and dominance or recessivenessrules. If an IDM2 value for a level-two characterization is greater thanthe IDM1 value for both demographic aspects comprising the level-twodemographic characterization when viewed alone, that value is said tocorrespond to an additive rule. If an IDM2 value for a level-twodemographic characterization is less than the IDM1 value for bothlevel-one demographic characterizations when viewed alone, that value issaid to correspond to a subtractive rule. If an IDM2 value for alevel-two demographic characterization falls between IDM1 values for therespective demographic aspects of the level-two characterization, thatbehavior is deemed to be dominant/recessive, and the IDM1 value closestto the IDM2 value is deemed the dominant IDM1 value.

A multi-dimensional array can be kept which records rules appropriate toeach IDM relationship for each event over time. Such an array may alsobe extended to include rules comparing multi-aspect combinations.Statistical tests can be applied to determine confidences with which anaspect or combination of aspects is related to another aspect orcombination of aspects by any of the given rules. Weights may beassigned to each rule, with a preferred embodiment using linearly higherweights to represent exponential rule growth.

Values produced by such a system may be stored in an array, orrecombination matrix. Rules can be applied additively for a demographiccharacterization of a particular level from lower level IDM solutions atwhich such rules are identified. Final confidences can be determinedthrough Pearson-r correlation with IDM calculations. Demographiccharacterization recombination matrices can aid in the calculation ofprobability ranges.

A mean of each demographic specification's event matching representationmay be stored over time in aspect representation indices. Suchone-dimensional arrays can also be used in confidence determinations forinformation obtained through other portions of the present invention.These indices can be updated as IDM calculations continue through STBevent time. Aspect representations can give the present inventionapproximate sample sizes for behaviors of each demographiccharacterization.

Aspect representation indices can also be used when determiningindividual behaviors. To determine such behaviors, probabilities can beassigned to each demographic specification and each STB event. If an STBmatches an event, corresponding probabilities can be ascribed to an STB,where such probabilities are normalized so the highest probability isunity. If an STB does not match an event, probabilities ascribed to suchan STB may be the linear inverse of probabilities ascribed to an STBmatching an event. For each STB and demographic specification at a giventime, summing ascribed probabilities for each demographic specificationand dividing by the number of probabilities can compute the probabilitythat an STB corresponds to a given demographic specification. A one-wayanalysis of variance can then be performed on such data to determine thelikelihood of such data representing a user of a respective STB.

As previously discussed, confidences with which a demographicspecification can be linked to an STB can be useful in refining datagenerated by the present invention. This usefulness arises out of thefact that combining relative confidences with a level-n demographiccharacterization yields a level-n+1 demographic characterization. Suchrelative confidences are also useful when evaluating assumptions, orrules, generated by the present invention. Such assumptions may begenerated in the above-described aspect recombination rules,specification similarities, aspect representation, and individualbehavior determination processes.

Information developed by each assumption has an empirical validity,which can be converted to a statistical confidence. This empiricalvalidity can be determined over STB event time by the assignment ofexpectation values, tracking of empirical values, and throughtime-correlation tests between expectation and empirical values. Eachassumption's validity may require determination through a slightlydifferent statistical formula.

Assumption validities, along with sample numbers and sample values, canprovide probabilities and ranges for information concerningdemographically specific groups. The present invention may translateinformation into a chosen confidence interval, then, for eachspecification and each process performed, sets of individuals matchingand not matching all possible events can be produced.

Through this process, each demographic specification group can belabeled with statistical “guesses” at a final range of values for anevent rating. A system of linear equations can then be solved to furtherreduce the ranges and fill in residual gaps left by such processes.Demographic numbers for each demographic characterization can also beused to further reduce the ranges by this possibilities reductionsystem. By way of example, without intending to limit the presentinvention, imagine a set of viewers matching a set-top box eventcontains 300 people who are Asian, and this same set contains 200 peoplewho earn over $80,000 per year. If it is known that all people in theset who earn over $80,000 per year are Asian, then the 100 remainingAsian people can be placed into categories corresponding to incomesbelow $80,000 per year. Some of these categories may already be reduced,so information can be filled in quickly.

Essentially, while an IDM and other processes of the present inventionmay have increasingly low confidences as demographic specificity levelsincrease due to low sample representation for many of demographiccharacterizations at these higher levels, a possibilities reductionprocess can take advantage of these low numbers by filling indemographic characterizations with certain matches or non-matches of STBevents. A possibilities reduction system can accomplish this becausedemographic data is known at these levels, and thus reduces the set ofpossibilities remaining.

Ranges can be reduced even in cases where demographic characterizationnumbers are not filled completely. The possibilities reduction systemmay use linear algebra rules to describe the remaining possibilities interms of mathematical symbols after each piece of information isconsidered. A matrix of specific probability ranges and theirmathematical relationships with other specific probability ranges for agiven event can be iteratively updated in a pre-determined order untilsuch iterations do not significantly change any matrix values.

Ultimately, ranges that cannot be further reduced may not be modified bythis iterative process without first modifying the confidence level. Forexample, by decreasing the confidence level (for example, from 90% to88%), ranges may be reduced and some demographic characterizations befilled in. An iterative process can then go through allcharacterizations and iteratively again reduce value ranges as much aspossible. At some point, the confidence level in the matrix may beincreased to its original level. However, additional mathematicalprocedures may be introduced to avoid an error function of the of thismatrix getting trapped in a (spurious) local minimum.

It is important to note that even if a customer is only satisfied withconfidences associated with level-five data, the relevant level-fivecharacterizations can be composed of any five demographic categoriesabout which the present invention collects demographic data and in whicha customer may be interested.

In addition to a probability reduction process, a preferred embodimentof the present invention may further reduce the likelihood of errors byemploying a Monte Carlo-type fit to such data after a probabilityreduction process has been applied. Such a fit may account for biasissues associated with data collected by the present invention. Thepresent invention may further address bias issues by monitoring set-topbox usage and identifying inconsistent behavior. For example, thepresent invention may detect when households leave a set-top box onwhile playing a DVD, videotape, or even while on the telephone or whenout of the home for extended periods by analyzing usage patterns foreach set-top box. By way of example, without intending to limit thepresent invention, the present invention may define “special events”which correspond to situations in which a particular set-top box remainson but no state changes occur over a period that is significantly longerthan an average state-change interval for that set-top box.

The present invention may also be integrated with recording systems,such as the Replay-TV and Tivo systems, to allow more detailed analysisof consumer behavior. Recording systems may be of interest in thepresent invention because of the level of control a user has over agiven program, including the ability to pause live programming.

An alternative calculation method which may be used by IDGM CalculationAlgorithm 940 essentially involves a more additive, yet thoroughapproach based on the demographic assumption to determine DATA2 valuesfor DATA1 data. However, rather than defining an error function to beminimized, this method represents a lengthy process of linear algebra byessentially comparing each zip code to each other zip code for eachdemographic characterization in a tailored customer query. Thiscalculation method is an alternative mathematical method that solves thesame problem as the previous method.

The present invention may essentially translate customer queries into aquery of the type “What percentage of a certain demographicspecification performed some action during a program state?” andcalculate the result of such queries. Definable Viewing Units (“DVU's”)include whole or partial content that can be matched to time andlocation. Customer queries can take the form of combination of actionsand DVU's.

Customer queries can be translated to a final query throughdetermination of a plurality of values within subsystems of the presentinvention and then operating on those values to derive a final queryvalue. Subsystem query values can be determined by a process computer,which employs multiple sub-processes to determine each sub-value. Aprocessor can convert any percentages generated by such sub-processes tonumbers, prorate and extrapolate such numbers to include non-sampledindividuals, and accounts for those STB's at presenting content tomultiple viewers.

A process computer may receive queries specific to a demographiccategory. A process computer can break apart a query into twocomponents; a set of zip codes for which the query will return data, andall other query components. For each zip code in a query, which may bereferred to as a “query zip,” the query zip can be matched against otherzip codes for which the present invention collects market data. Thisprocess will result in n−1 zip code combinations (“zip-zipcombination”), where a query zip is the first combination, and where nis the total number of zip codes about which data is collected by thepresent invention. These zip-zip combinations can be run through amulti-step process that determines final query values.

The first of these processes, CP1, determines weights for each zip-zipcombination. CP2 determines a value each zip-zip combination can inferfor the final query, or for some sub-query. CP3 can determine if thereis more than one pattern of inferred query values and, if there are,determines a set of values from the current dataset. CP4 reduces valuesfrom CP3 by one level. CP3 and CP4 can be iteratively acted upon untilonly one pattern is apparent, and a result set can be determined.

CP1 determines weights for each zip-zip combination based on a varietyof factors. One such factor is the percentage of a test zip code'spopulation that falls into the query category in question. In apreferred embodiment, a test zip code's population should be asdifferent as possible from a query zip. This provides a resolutionincrease, as the larger the differences between the categories, thebetter the effect of individual common points can be determined.

For each category other than zip codes, CP1 may give higher weight tothose categories for which the population of the test zip is similar tothe query zip. The importance of these other categories can bedetermined to make an overall determination of how similar the test zipis to the query zip in terms of such other categories. By way ofexample, without intending to limit the present invention, if testzip(1) is ninety percent similar by age distribution and ten percentsimilar by religious affiliation, test zip(1) will likely be assigned ahigher weight than one that is ten percent similar by age distributionand ninety percent similar by religious affiliation. The relevance ofeach category, and hence its weight, can be determined by the effect acombination of zip codes seem to have on market share for a given query.

The present invention can determine a weight for a given test zip by theformula Weight=(c)(W), where c is the percent difference between personsbelonging to a query category in a test zip and those belonging to thesame query category in a query zip.

As an example, without intending to limit the present invention, if CP1received a query including query category ‘African American’, and thefollowing were true for sample query zips and test zips:

query zip: 10% AA+90% other=Market Share=20%

test zip: 20% AA+80% other=Market Share=40%,

then c may be calculated as a percent difference between 10.0 and 20.0.

W can be determined by summing weights assigned to each “other”category, where other categories are defined as categories in the unionset of categories between a query zip and test zip, except for a querycategory or any sub-categories defining a query category. In equationform, this can be written as W=sum(w[x])=sum (w[1] . . . w[x]), where xis a numerical identifier of each category, and x is incremented overthe number of categories.

For each category:

Let % d(x,y)=the absolute value of a percent difference between ‘x’ and‘y’

Let a=% d(A[0], A[n]), where A[0] represents a demographic percentage inquery zip for a given demographic category, and A[n] represents ademographic percentage in a test zip for a given demographic category.

Let b=% d(B[0], B[n]), where B[0] represents a market share for a givendemographic category in a query zip, and B[n] represents a market sharefor a given demographic category in a test zip.

Let each category weight w(x)=q(a)×q(b)

where q(a)=the weight of ‘a’ and q(b)=the weight of ‘b’ where:

q(a)=f(a)=approximately 1/a

q(b)=f(a,b)=a function in which:

as ‘a’ goes to 0 and % d(a,b) goes to 0, q(b) goes to infinity, and

as ‘a’ goes to infinity and % d(a,b) goes to 0, q(b) goes to 0.

For example, if a query category is African American, categories in acomplete union set of categories between the query zip and test zipwhich do not include African Americans should be reviewed. One suchcategory may be White Male, and by way of an example, the following maybe true:

query zip: 24% WM+76% other=Market Share=20%

test zip: 30% WM+70% other=Market Share=40%

The statistics above can be rewritten as follows if the “other”categories and their respective percentages are disregarded:

query zip: A(0) WM=B(0), where A(0)=24%, B(0)=20%

test zip: A(n) WM=B(n), where A(n)=30%, B(n)=40%

An optimum weight function for a test zip code is an intrinsic propertyof its relationship to a query zip. q(a) can be seen as a measure of apercent similarity of a given category between query and test zips, andq(b) can be seen as a measure a category has on describing market sharedifferences between query zip and test zip. Thus, categories receivingheavier weights are those in which % d(A[0], B[n]) is low or null and atthese low values % d(B[0], B[n]) is a similar trended value. In apreferred embodiment, As % d(A[0], B[n]) increases, that % d(B[0], B[n])should decrease.

A natural function fitting q(a) and q(b) requirements will be one thatprovides a proper optimization of zip codes, such as a Laplacianfunction. It is clearly a symmetrical surface in 3D space with fourspecific boundary conditions. If a specific function can not be foundthat provides such a fit, a practically optimal function can be createdthrough power series.

CP2 can evaluate the percentage of persons falling into a query categoryin a query zip and the percentage of persons falling into the querycategory in all test zips, and use this to evaluate specific marketshare differences between the query zip and each test zip codes. CP2then evaluates the information about these two zip codes, andestablishes a best guess as to the query category differences thatcontributed to an observed market share difference.

CP2 can take demographic and market share information corresponding toindividual zip codes and solve a set of linear equations for variablesinvolved. One variable resulting from such a solution can be related toa percentage of persons falling into a query category for a given zipcode or set of zip codes. Another such variable may be the percentage ofpersons in a zip code not falling into a category. CP2 may set theright-hand side of each equation to the market share of a query DVU.

By way of example, without intending to limit the present invention, agiven zip code or set of zip codes may yield a formula such as:10AA+0.90o=0.20. Such a formula may indicate that a zip code or set ofzip codes has a demographic makeup that is ten percent African Americanand ninety percent “other.” To further ease understanding of thisexample, assume the DVU is the percentage of TV sets that watched all ofthe show ER from 10.00.00 to 11.00.00 on Nov. 11, 1999. In the aboveexample, the market share for this DVU is 20 percent. It should be clearto one skilled in the art that in the equation above, the variable ‘AA’refers to the percentage of African American persons in a given zip codefulfilling DVU criteria.

A preferred embodiment of present invention may represent such anequation using the following three matrices for computational clarityand storage efficiency:

[AA o] [10 90] [20]

CP2 may translate information for all query and test zip codes into thisform for a query category and for other categories in which none of thepersons making up those category percentages fall into a query category.If the zip code, category, and DVU example above were expanded toinclude a test zip(n), there would be 3 matrices again, but this time2-dimensional:

[AA o] [10 90] [20]

[AA o] [12 88] [19]

Relationships between such matrices and their resulting linear equationscan be seen when the linear equations are represented graphically by aline in 2-dimensional space. If two lines are on the same plane, suchlines must either 1) be the same line, 2) be parallel lines, or 3) crossat some point. Through these relationships, CP2 can create a “bestguess” at a mutual answer for variables in the equations based oninformation the equations imply. This guess need not fall onto a pointin the interval of either equation.

This process can be seen as analogous to drawing a set of lines in2-dimensional space and then finding intersections of these lines. Thisintersection may occur once per two dimensions, indicating twoparticular variables. The first of these variables relates to aparticular category of interest, and the second variable is always‘other’.

Statistically, with several test zips with which to work and each with agiven number of samples, CP2 may be seen as analogous to sampling a muchlarger number of samples than are in any individual sample zip, anddrawing a normal curve for each. The resulting curves may then have eachcurve subtracted from them, and one normal curve may be built from thedifferences. In some cases, this curve may be a line representing asingle value, rather than a curve.

At this point, the normality of an extrapolated curve is no longer anassumption, as only one specific category variable is of concern at agiven time. Any “other” values not under the peak of a curve represent amathematical confidence level based only on sample size versuspopulation error, and not a statistical skewing possibility.

CP3 can take information from CP1 and CP2 in the form of a series oftest zip codes and the best guess value for each. Based on this data,CP3 can then determine whether such best guesses exhibit aone-dimensional pattern. If such a pattern exists, CP3 can return thepeak of the pattern as the query return value. If such a pattern is notexhibited, CP3 may pass data to CP4.

CP4 takes CP3 data and attempts to establish a one-dimensional patternfrom it. CP4 can run multiple queries through CP1, CP2, and CP3 usingeach zip code in the present invention's sample population as a queryzip. For each iteration through CP3, a phantographic category percentagecan be assigned to each zip code. These can be reinserted to CP1, CP2,and CP3 using a query zip and its assigned phantographic categories inplace of demographic category percentages.

With each iteration, the complexity of CP4 data is reduced by one level.When CP4 data exhibits a one-dimensional pattern, this result can bereturned as a final query value. If CP4 data exhibits more than aone-dimensional pattern, the data can be fed back through CP1, CP2, andCP3 for additional analysis, using new phantographic categorypercentages with each iteration. Through this process, CP3 and CP4 canidentify those demographic categories having a high effect onviewership.

As previously described, CP3 can receive data from CP1. Such data may bein the following form:

test zip 1 zip 1 weight test zip 2 zip 2 weight test zip 3 zip 3 weight. . . test zip n zip n weight total = 1.00

In addition to data from CP1, CP3 may also receive data from CP2. SuchCP2 data may resemble the following:

test zip 1 query zip - zip 1 linear equation solution value test zip 2query zip - zip 2 linear equation solution value test zip 3 query zip -zip 3 linear equation solution value . . . test zip n query zip - zip 3linear equation solution value

CP3 can multiply solution values for each zip code by the weight of thatzip code to get a sub-answer. These sub-answers can then be summed toyield a final answer.

While CP3 can generate query results based on linear equation solutions,CP3 can also generate and analyze graphs of zip code weights (w) versuslinear equations solution values (v). An example of such a graph isillustrated by FIG. 12.

CP3 may reduce the influence of random noise on such a graph byassigning each w the average of its value and the values on either sideof it. This procedure may be repeated until all noise is reduced toacceptable levels. The result would be a graph with one of the followingconditions:

one defined peak from one direction;

one defined peak from two directions;

more than one defined peak, possibly with varying heights; or

a random curve.

If a curve exhibits conditions outlined in numbers 1 or 2 above, anear-perfect guess at an ultimate query return value can be made. Such avalue may be based on a peak value observed, or may result fromextrapolation of a graph over a larger data range.

If a curve exists exhibiting conditions outlined in number 3 above,which is illustrated by FIG. 12, then at least one additional categorymay be effecting query data. A random category can be created, called aphantographic, and relevant percentages can be assigned to that categoryto account for peak distributions observed in the graph. CP4 can thenfeed this phantographic category back into CP1 and CP2 for the query inquestion to determine an appropriate query return value.

By way of example, if the data in FIG. 12 represented a 60/40distribution, the 60 percent distribution may be assigned to zip-zipcombinations where test zips are directly under parts of the graphassociated with the 60 percent peak. The 40 percent distribution may beassigned to zip-zip combinations where test zips are directly underportions of the graph associated with the 40 percent peak. Test zipsfalling under valleys between such peaks may be ignored. Through thissystem, the present invention should account for categories affectingmarket share differences without requiring tracking of a large number ofcategories.

As CP3 feeds data to CP4, CP4 can assign phantographic categorypercentages to each zip code, then route the result through CP1, CP2,and CP3, thereby reducing underlying patterns by one level. Thefollowing is an example of calculations and procedures employed by CP4.

FIG. 13 is a sample graph generated by CP3 for a query category, zipcode, and market share. CP2 values are arranged in increasing order fromleft to right, with higher lines indicating heaver weighting for a givenvalue. When CP2 generates two or more of the same value, the weightrepresented on such a graph may be increased to the sum of theindividual weights for all matching values.

FIG. 14 provides a more detailed view of the graph in FIG. 13. The sumof all weight values in the graph should total 1, and all weights mustfall between 0 and 1. This is due to the prorating step performed afterCP1. FIG. 15 provides an additional view of FIG. 14, with valuesprovided for various points on the graph, and zip codes numbered 1through 25. The following is a table of values illustrated by FIG. 15:

Zip Code weight (to 3 significant digits) value (to 2 significantdigits) 1 0.402 0.50 2 0.515 0.79 3 0.027 0.23 4 0.040 0.71 5 0.038 0.906 0.070 0.29 7 0.054 0.41 . . . . . . . . . 25 0.052 0.26

In a more abstract sense, data passed from CP3 to CP4, when graphed, mayresemble FIG. 16, FIG. 17, or FIG. 18. FIG. 16 illustrates CP3 dataresulting in a graph with a single peak. FIG. 17 illustrates CP3 dataresulting in two peaks. FIG. 18 illustrates CP3 data with many peaks.While CP3 data may resemble one of these three figures, CP4 does notdistinguish between such cases.

If, for a given query, CP3 resulted in a graph similar to FIG. 16, thismay result in a great deal of confidence in a query result value. Such avalue may be either the sum of the means of each value times itsrespective weight or simply the value underneath the peak of the graph.Assuming a symmetrical and “natural” weighting function or algorithm,CP3 data yielding FIG. 16 would provide validity to the assumption thatpersons with more similar graphic makeup have more similar televisionviewing patterns. The present invention can answer the questionscustomers truly intend to ask, as any quality shared by that group aloneis represented in the CP1,2,3 algorithm. This alone can represent asignificant improvement over statistical methods employed in the priorart.

Rather than the single-peak graph of FIG. 16, if CP3 were to result in agraph similar to FIG. 17, the correct answer is not one that would begiven by a full average of all data, as this would provide a valuebetween the two peaks. The correct answer is the value on the V axisdirectly under the first peak, or the value on the V axis directly underthe second peak. CP4 can select an appropriate value from two or moresuch values.

The fact that there are two peaks in FIG. 17 indicates that anotherdemographic category should have been part of the graphic data. When twopeaks exist such as in FIG. 17, there is only one difference between theset of values forming the peak on the left versus the set of valuesforming the peak on the right. While CP4 may not determine what thisdifference relates to, CP3 data is based on zip codes, and thedifference is likely to be geographic. For example, it may be that thepeak on the right is formed by zip codes in coastal cities, and the peakon the left is formed by non-coastal cities. Regardless of the source ofthe difference, FIG. 17 clearly illustrates its existence.

If all graphic categories are ignored except the query category, a newset of categories, called phantographic categories, can be assigned totest zips making up the chart distribution. A value of 0 can be assignedto test zips creating the left-hand peak, and a value of 1 can beassigned to those test zips creating the right-hand peak. If this dataset is now run through CP1, CP2, and CP3, a similar but more dispersegraph should result. If all zip codes sampled by the present inventionwere iterated through as query zips, and all others were test zips, andthe value belonging to each peak was assigned to those test zips beneathit as the phantographic category percentage for each query zip in thesystem, this data could be run through CP1, CP2, and CP3.

This would result in a new graph for the query zip. Such a graph shouldresult in all phantographic categories having similar percentagedistributions for a given zip code. Further, a percentage exhibited by aquery zip can be determined, and thus the initial peak associated withthe query zip can be properly selected by CP4.

The present invention may further employ a “category generator”, whichcan search demographic, geographic, and other databases for a graphicdistribution percentage matching any recurring phantographicdistributions. While many are likely to remain undiscovered, if one isfound it may be added to a list of categories monitored by the presentinvention.

Due to CP4, patterns in CP3 data cannot escape the present invention.Even algorithms at the heart of data generated by a random numbergenerator could be determined through CP4's iterative processes.

FIG. 5 is a block diagram of modules used in Graphic Data acquisition.As FIG. 5 illustrates, the present invention may acquire data forInterval-updating Graphic Database 620, and ultimately for IDGM GraphicMatrix 910, from outside sources such as Graphic Vendor 610. However, asalso illustrated in FIG. 5, the present invention may also garner datafor Interval-updating Graphic Database 620 from Evolving STB ViewerPossibilities Database 960.

Evolving STB Viewer Possibilities Database 960 may include, for eachset-top box, behaviors and demographics associated with one or moreregular users of said set-top box. Evolving STB Viewer PossibilitiesDatabase 960 may also identify a set of set-top boxes whose monitoredbehavior best fits demographics for a given geographic region. In apreferred embodiment, regional demographics may be complied fromspecification percentages of such set top boxes. In an alternativeembodiment, specification percentages may be fit by the weight of theirmagnitudes. The present invention may correlate and assign demographicsto set-top boxes, and the sum of these can be combined to indicatedemographics for a specific region.

While Interval-updating Graphic Database 620 may initially receive datafrom Graphic Vendor 610, Interval-updating Graphic Database 620 may notalways require such data. As the quality of data stored in Evolving STBViewer Possibilities Database 960 increases, the present invention mayno longer require data from Graphic Vendor 610.

Block 460 identifies a process that produces data which may raiseprivacy concerns. As illustrated in FIG. 5, the present invention canprotect such data by restricting access to such data to only componentsof the present invention. Information stored inside Block 460 can onlybe accessed by customers through components of the present invention,such as Block 940.

FIG. 6 is a block diagram illustrating modules used by an IndividualBehavior Determination System of the present invention to acquireindividual behavior data. As illustrated by FIG. 6, IDGM CalculationAlgorithm 940 may extract event data from Tuner Data Center 930. IDGMCalculation Algorithm 940 may also perform event analysis.

As with other parts of the present invention, an individual behaviorsystem may use the demographic assumption at its core. An individualbehavior system may also assume that statistical determinations can bemade that define a viewer of an STB by measuring STB states over timeand ascribing probabilities to an STB. Individual viewers of an STB canthen be statistically identified based on behaviors exhibited on an STB.

The mean of all ascribed specification percentages may be kept inEvolving STB Specification Percentage Database 950, illustrated in FIG.6. Evolving STB Specification Percentages Database 950 may comprise, foreach specification, a percentage of time that a behavior associated witha set-top box matches a particular specification. Viewed over time,Evolving STB Specification Percentage Database 950 may contain a “fuzzy”percentage, or Identity Percentage, for each specification, which may bedetermined for each set-top box.

The “Attribution Percentage” ascribed to an STB for a given querybehavior ‘n’ can be calculated through a piecewise function:

AP (n)=

If YES: behavior match % of that specification;

If NO:

$\left( {{1/{Process}}\mspace{20mu} 1\%\mspace{20mu}{of}\mspace{20mu}{spec}} \right)/{\sum\limits_{1}^{s{(c)}}\left( {{1/{Process}}\mspace{20mu} 1\%\mspace{20mu}{of}\mspace{20mu}{spec}} \right)}$

Where s(c) is the total number of mutually exclusive specifications ofthe query category/categories.

The “Identity Percentage” of any demographic specification at any time(t) is simply:

${I\; P_{(n)}} = {\sum\limits_{1}^{n{(t)}}{A\;{P_{(n)}/n}}}$

Where n(t) is the total number of queries to date involving the zip codein which the sample exists.

The following is included by way of example, without intending to limitthe present invention. Below are five sample queries submitted to thepresent invention over the first two weeks of operation, and sampleresults generated by the present invention. These queries involve thecategory “race” for the zip code 34208.

Query #: 1 Behavior: watched >80% of Friends, 8:00 pm-8:30 pm on Mar.23, 2000 Black: 08% 16% of behavior matches White: 23% 46% of behaviormatches Asian/Pacific 12% 24% of behavior matches Islander: Other: 07%14% of behavior matches Query #: 2 Behavior: muted channel at firstcommercial of Lakers vs Suns, 8:00 pm on Mar. 24, 2000 Black: 02% 10% ofbehavior matches White: 10% 50% of behavior matches Asian/Pacific 05%25% of behavior matches Islander: Other: 03% 15% of behavior matchesQuery #: 3 Behavior: watched >80% of Friends, 8:00 pm-8:30 pm on Mar.30, 2000 Black: 27% 54% of behavior matches White: 03% 6% of behaviormatches Asian/Pacific 12% 24% of behavior matches Islander: Other: 08%16% of behavior matches Query #: 4 Behavior: watched >80% of Seinfeldrerun, 7:30 pm-8:00 pm on Mar. 31, 2000 Black: 10% 17% of behaviormatches White: 24% 40% of behavior matches Asian/Pacific 14% 23% ofbehavior matches Islander: Other: 12% 20% of behavior matches Query #: 5Behavior: watched >40% of Seinfeld rerun, 7:30 pm-8:00 pm on Mar. 31,2000 Black: 22% 28% of behavior matches White: 11% 14% of behaviormatches Asian/Pacific 23% 29% of behavior matches Islander: Other: 24%30% of behavior matches

To further refine this example, assume that a particular STB exhibitedthe above behaviors as follows, and thus percentages attributed to thatSTB for the category of “RACE” were as follows:

Resulting AP(n) Contribution Behavior #: n Exhibited BLA WHI API OTHBehavior #1: 1 YES   16%   46%   24%   14% Behavior #2: 2 YES   10%  50%   25%   15% Behavior #3: 3 NO   06%   58%   14%   22% Behavior #4:4 YES   17%   40%   23%   20% Behavior #5: 5 NO   23%   46%   22%   8% ΣAP(n)   72%  240%  108%  79% IP(n) = ΣAP(n)/5 14.4% 48.0% 21.6% 15.8%

Below are the results of a sixth sample query submitted to the presentinvention and sample Identity Percentage (IP) calculations for the“race” category:

Query #:6 Behavior: watched >90% of Law & Order rerun, 7:00 pm–8:00 pmon Mar. 31, 2000 Black: 02% 08% of behavior matches White: 14% 56% ofbehavior matches Asian/Pacific Islander: 04% 16% of behavior matchesOther: 05% 20% of behavior matches Resulting AP(n) Contribution Behavior# n Exhibited BLA WHI API OTH Σ AP(n − 1) N/A   72%  240%  108%   79%1–5 Behavior #6: 6 YES   8%   56%   16%   20% Σ AP(n)   80%  296%  124%  99% IP(n) = Σ AP(n)/6 13.3% 49.3% 20.7% 16.5%

These results would seem to suggest that STB corresponds to “White.” Sixqueries may not be enough to decide such a conclusion. The probabilitythat a person actually is some IP(n) specification can be derived simplythrough straightforward statistical methods.

Referring again to FIG. 6, the number of viewers for a set-top box maybe constrained by Interval-Updating Graphic Database 620. Evolving STBSpecification Percentages Database 950 can receive input from IDMCalculation Algorithm 940, which may run continuously on alltime-possible events.

The present invention may also utilize a second evolutionary database,illustrated in FIG. 6 as Evolving STB Viewer Possibilities Database 960.A goal of Evolving STB Viewer Possibilities Database 960 is not todetermine who is using a set-top box, but rather who may be viewingparticular content. Thus, Evolving STB Viewer Possibilities Database 960may track possible or probable users of a particular set-top box,regardless of whether an individual, a couple, a family, or a largegroup of people is watching. Evolving STB Viewer Possibilities Database960 may be updated at regular intervals, but optimally may be updated atevery update of Evolving STB Specification Percentage Database 950.

Data from Evolving STB Specification Percentages Database 950 may bebest fit to the demographics of an area, and this best fit may be heldin Interval-Updating Graphic Database 620. A best fit may be calculatedby spot-filling, in which the highest set-top box specificationpercentages fill the most demographically significant spots first. Thisspot-filling process continues until all spots are filled. At any time,if a category has been completely accounted, no more spots may be takenand any relevant specification can be discarded.

As a result of such calculations, Interval-Updating Graphic Database 620ultimately holds a “best guess” at the complete specification makeup ofeach individual in a household. Spot-filling may not be the mostaccurate technique, however, as a best fit reduces overall deviation.Due to higher specification percentage weighting, spot filling mayprovide an extremely close approximation.

The evolving database and best guess technique outlined above have beendescribed in examples which determined viewing behaviors for a givenhome. However, the present invention can also account for persons in abar watching a football game on Sunday, even if they are already countedat home.

To determine individual persons, and not just a group percentage,matching a behavior, current set-top box viewer possibilities may bebest fit to IDM calculation specification percentages for any event forthe population of some graphic region or set of regions. In this way,individual behaviors can be determined. This best fit may be performedby spot-filling in a manner similar to that outlined above. Every personcan be accounted for by the present invention, whether are at a bar, aneighbor's home, or their own home. The IDM specification percentage maybe fit to evolutionary specification percentages for each box, therebyaccounting for such deviations.

Ultimately, for each event, individuals matching a behavior may beknown, and such data may be sent to Individual Behavior Determiner 970and stored in Individual Behavior Database 290. Individual BehaviorDatabase 290 may hold all individual behaviors recorded since samplinginception. Individual Behavior Database 290 may comprise a database oftime-oriented arrays containing information about what each sample hasdone since sampling inception. Individual Behavior Database 290 maycomprise a database approximating individual behaviors for each eventfrom IDM Calculation Algorithm 270 or 940.

Individual Behavior Determiner 970 comprises a linear system that canfind a best fit between IDM Calculation Algorithm 270 or 940 andEvolving STB Viewer Possibilities Database 960. Individual behaviors maybe approximated as a best fit of the data groups from IDGM CalculationAlgorithm 940 and Evolving STB Viewer Possibilities Database 960 foreach event. In this manner, the behavior of one individual may betracked over time.

New specification percentages from Evolving STB Percentages Database 950may be continuously available and periodic recalculation of individualbehaviors may be preferred. Percent changes in Evolving STB PercentagesDatabase 950 may be small and determinable through time, and this may bethe factor used to determine the interval of individual behaviorrecalculation.

FIG. 7 is a block diagram of modules comprising Future Events QuerySystem 190 of FIG. 1. Future Events Query System 190 may be differentfrom Past Events Query System 200. Future Events Query System 190 maycomprise a web-based system, which interacts with Post TranslationSystem 420, allowing Market Customer 530 to query the present inventionregarding behaviors that are most likely to occur in the future. FutureEvents Query System 190 may include a web-based system with a naturallanguage, graphical, or command-line interface, providing the customerwith the ability to extract information from the system.

Individual Behavior Database 290 can contain individual-specificbehavior information as determined by the present invention. Individualbehaviors may be analyzed by Series Analysis System 980, which cancomprise a system that looks for data trends and patterns (both directedand undirected).

Series Analysis System 980 may comprise an algorithm looking for trendsin individual and group viewing behaviors that may efficiently definerelevant behavior patterns. Such series analysis algorithms may be timebased, defined behavioral events, and undefined behavioral events, suchas a straightforward series that determines behavioral patterns. Thesealgorithms may take individual behaviors from Individual BehaviorDatabase 290 and look for trends based on time, content, channelchanges, and the like. Series Analysis System 980 may also definerelevant events according to results of its analysis.

Relevant viewing events may comprise events that represent some patterndescribing viewing behaviors. Events may be pre-defined events such as“changing the channel at the end of a show” or “changing the channel atthe beginning of a commercial,” or they may be definitions that are morearbitrary, such as “changing the channel twice in 5 seconds beforechanging the channel 3 times in the next 10 seconds.” Events may resultfrom individual behavior data mining based on series analysis andbehavior pattern determination, and then reporting such patterns in asimple form. By way of example, the present invention may learn that aparticular graphic category may not get home until 6:00 pm, then views anews channel for half an hour, then turns the set-top box off for anaverage of a half hour, presumably for dinner.

One series analysis that may be performed by the present invention is aTime Series Analysis (“TSA”). In a TSA, trends that can be fullydescribed as a function of time may be identified. This distinction ismade since most mathematical series analysis methods are usuallyreferred to as ‘time-series analyses.’

An alternative series analysis that may be performed by the presentinvention is a Defined Event Series Analysis (“DESA”). A DESA canidentify trends which may be fully-described as a function of a set ofbehaviors, such as changing the channel near the beginning or end of anhour or near the beginning or end of certain content, watching entireprograms, watching certain genres, and the like. A DESA can allow thepresent invention to identify not only those features that are ofinterest to the present invention, but also to customers of the systemas well.

Still another series analysis that may be performed by the presentinvention is a Undefined-Event Series Analysis (“UESA”). A UESA issimilar in many respects to a DESA, except that a UESA can look forgeneral trends while defining its own events. By way of example, withoutintending to limit the present invention, sampled individuals may be ontime schedules or have general viewing habits about which the presentinvention may soon learn.

After series analysis, events that the present invention identifies asdescribing behaviors in Individual Behavior Database 290 may be sent toEvent Definition System 400, where an emphasis may be placed onbehaviors that are more current. Event Definition System 400 can alsoaccept input from Updating Future Airings Database 990 which may hold acontent guide that may include content attributes and contentpresentation information. Such content presentation information mayinclude, but is not limited to, to networks or channels presenting suchcontent, and times such content was or will be made available.

Event Definition System 400 can break down programming into eventsdefined efficiently by Series Analysis System 980. Event DefinitionSystem 400 may comprise an algorithm that may accept a broad range ofcontent attributes. Event Definition System 400 may also break contentapart into a best fit of events as determined by Series Analysis System980.

Updating Future Airings Database 990 may comprise a database of arraysholding a best guess at what content may be aired at any time in thefuture. Airings Source 360 may continuously update Updating FutureAirings Database 990. For times relatively far into the future, generalextrapolations may be made to save data space.

Future content may be broken down in terms of viewership events existingin Event Definition System 400. From these datasets, Future EventsMapping System 450 may map future individual and group events ontofuture content by describing it in terms of the events defined by theEvent Definition System 400.

Future Events Mapping System 450 may comprise a simple algorithm thatlinearly forecasts the most probable events onto a map of futureprogramming. Future Events Mapping System 450 may comprise an algorithmthat takes input from Event Definition System 400 and maps probablemutually exclusive sets of behaviors which Series Analysis System 980forecasts for these events. Events of both systems may be identicallydefined, thus requiring only a best fit mapping of individual behaviorsonto the future programming.

Market Customer 530 may query Future Events Query System 190, which maycomprise a user-interface with options for tailoring such queries.Post-Translation System 420 can translate such queries into amathematical formula that may be understood by Pre-Translation System410. Such translations may simply express a query in a format thatfacilitates data extraction from Future Events Mapping System 450.

FIG. 8 is a block diagram of modules used in Program Entry and ProgramBuilder Systems of the present invention. Program Entry 540 mayfacilitate behavioral or viewership predictions for content which hasyet to be experienced by the public based on data entered by a customer.Program Entry 540 may allow a customer to enter attribute ranges forcertain content, and Program Entry 540 may report specific attributevalues which best fit a desired outcome. Program Entry 540 may convertsuch customer data to a format readable by Event Definition System 400.Event Definition System 400 may break down content to determine likelyviewership or other behaviors based on statistics generated by otherportions of the present invention.

Program Builder 430 can compile a content description, including variouscontent attributes, for content that is likely to be popular. Suchcontent descriptions may be based on events randomly pieced togetherfrom Event Definition System 400. Program Builder 430 may have anon-random component as well, in the form of an iterative system. Aniterative system may reduce processing times and increase the likelihoodof quality matches per unit time.

Event Definition System 400 can pull events that best describeindividual viewing behaviors and patterns from other portions of thepresent invention, and such events may be entered into to RandomGenerator 440. Random Generator 440 may comprise a random component forProgram Builder 430, and Random Generator 440 may piece together contentcombinations to build a hypothetical program.

Random Generator 440 may comprise an algorithm which can select fromcontent attributes and content components submitted to it in a dataset.Such selections may be performed in a computationally random manner,thereby allowing for a variety of dynamically generated content. In apreferred embodiment, Random Generator 440 may include an option,selected through Program Builder 430, which can mark as used thoseelements selected as part of a dataset, there by restricting therecurrence of such elements.

As with customer-generated content, probable content popularity forcontent generated by Program Builder 430 may be calculated throughProgram Entry 540. However, this calculation may also be run in aniterative or non-iterative cycle so that an optimal “proposed” programmay be described.

FIG. 9 is a block diagram of modules used in a Data Mining andPrediction System of the present invention. Data Customer 510 maycomprise a customer of the present invention interested in informationfrom Prediction System 240 or Graphic Correlation System 920.

Graphic Correlation System 920 may comprise a graphic correlationdatabase that may be updated by Series Analysis System 980. SeriesAnalysis System 980 can analyze correlations in graphic data alone,without respect to tuner data. Graphic Correlation System 920 may holdcorrelations determined thus far, and may generate additionalcorrelations. Data Customer 510 may use graphic Correlation System 920and its correlations.

Prediction System 240 may comprise both a system of algorithms whichdetermines statistical probabilities of DATA1, DATA2, DATA3 or otherbehaviors that have or will occur, and a web-based system allowing Datacustomer 510 to query the present invention about these behaviors ortrends. Such queries may be entered through a variety of means,including natural language, graphical, or command-line interfaces. Thecomputational algorithms of Prediction System 240 may be similar tothose of Series Analysis System 980, except that Prediction System 240may be concerned with general behavior patterns, and not necessarilythose having to do with television-related behaviors.

Sales Data 520, which may alternatively be seen as DATA3, may comprisesales or other operational information about business performance ortrends of customers, competitors, or an industry in general. Such datamay share features, such as a zip code, with a DATA2 counterpart. MarketCustomer 530 or other data sources may provide such data in a formatreadable by Series Analysis System 980. This data may comprise databased on sales figures for operations in certain geographic regions, orother information, such as colors or street locations of stores, phonenumbers, building types, and the like.

Market Customer 530 may comprise a customer interested in pastviewership data or a customer attempting to predict the desirability ofpreviously unaired content. In a preferred embodiment, Market Customer530 may provide additional data for Sales Data 520; such data may becross-referenced to commercial programming, competitors, and the like.

While a preferred embodiment of the present invention is geared towardmeasuring television viewership, the present invention may be useful forother purposes. For example, the Series Analysis algorithms used by thepresent invention may be run against Graphic Data, Sales Data 520, andthe like without respect to set-top box data. Through such analysis, thepresent invention may provide detailed demographic data in addition to amarket research services. Such analyses may be extended to look fortrends in demographic data, thus further refining the understanding of ageographic region for advertisers, government agencies, and othersinterested in such data. This analysis could effectively be used tofurther define the effects of commercial programming, to moreappropriately plan cities and city services, and other such purposes.

Weighting/Specification Selector 250 may determine graphic categories ofinterest to such parties by evaluating values from Evolving STBSpecification Percentages Database 950. Low-relevancy categories may berejected and new categories can be selected or introduced forevaluation. Weighting/Specification Selector 250 can determine graphiccategories that may be most relevant to viewing behavior, and maycomprise an algorithm that weighs categories according to sets ofspecification percentage sums.

Higher mutually exclusive category sums, built on a smaller number ofspecification percentages, may create a higher weight. A formula may beprovided that determines whether a category should be excluded in thenext update of IDM Graphic Matrix 910. Depending on the nature of IDMCalculation Algorithm 270 or 940, weights may be included in thecalculation system rather than responsible for category exclusion in theIdentity Matrix.

While the preferred embodiment and various alternative embodiments ofthe invention have been disclosed and described in detail herein, itwill be apparent to those skilled in the art that various changes inform and detail may be made therein without departing from the spiritand scope thereof.

1. A system for predicting future events based on a proposed dataset,consisting of: a dataset of past events, wherein the dataset of pastevents includes set-top box event data that is collected without knowingindividual-specific demographic information; a known dataset sharing atleast one attribute with the dataset of past events, and withsubstantially similar attributes to the proposed dataset; a means ofcorrelating the dataset of past events with the known dataset to form anew dataset; and, a means of correlating the new dataset to the proposeddataset.
 2. The system of claim 1, wherein the known dataset of includescontent attributes and content presentation data.
 3. The system of claim2, wherein the proposed dataset of includes a set of content attributesfor proposed content.
 4. A system of predicting future events for agiven demographic segment, comprising: a dataset of past events, whereinthe dataset of past events includes set-top box event data that iscollected without knowing individual-specific demographic information; ademographic dataset sharing at least one attribute with the dataset ofpast events; a means of correlating the dataset of past events with thedemographic dataset and storing the result in an array; a known datasetsharing at least one attribute with the demographic dataset, and withsubstantially similar attributes to the proposed dataset; a means ofcorrelating the array with the known dataset to form a new dataset; and,a means of correlating the new dataset to a proposed dataset.
 5. Thesystem of claim 4, wherein the demographic dataset shares at least oneof a zip code and other geographic identifier with the set-top box data.6. The system of claim 4, wherein the known dataset shares at least oneof a zip code or other geographic identifier with the array.
 7. Thesystem of claim 4, wherein the proposed dataset comprised of includesproposed content and attributes corresponding thereto.
 8. A system ofpredicting future events for a given demographic segment, comprising: adataset of past set-top box events, collected manner without knowingindividual-specific demographic information; a demographic datasetsharing at least one attribute with the dataset of past set-top boxevents, such as a zip code or other geographic identifier; a means ofcorrelating the dataset of past events with the demographic dataset andstoring the result in an array; a known dataset sharing at least oneattribute, such as a zip code or other geographic identifier, with thedemographic dataset and with substantially similar attributes to theproposed dataset; a means of correlating the array with the knowndataset to form a new dataset; and, a means of correlating the newdataset with a proposed dataset comprised of includes proposed contentand attributes corresponding thereto.
 9. A method of predicting futureevents for a given demographic based on a proposed dataset, comprising:monitoring past events, wherein the dataset of past events includesset-top box events that are monitored without knowingindividual-specific demographic information; correlating the past eventswith a demographic dataset and storing the result in an array;correlating the array with a dataset sharing at least one attribute withthe array, and with a substantially similar structure to the proposeddataset, the results of such correlations ere being stored in anadditional dataset; correlating the additional dataset with the proposeddataset; and reporting the results of such correlations as a predictionof future events.
 10. The method of claim 9, which wherein thedemographic dataset and the set-top box event dataset contain zip codeattributes.
 11. The method of claim 9, wherein the proposed datasetincludes proposed content attributes.
 12. The method of claim 9, whereinthe past events include attributes of any content being experienced atthe time of an event.
 13. A method of predicting future events for agiven demographic based on a proposed dataset, comprising: monitoringpast events, including set-top box events, a zip code associated with aset-top box, and attributes of content presented by the set-top box atthe time the event occurs, manner without knowing individual-specificdemographic information; correlating the past events with a demographicdataset which includes at least one zip code, and storing the result inan array; correlating the array with a dataset sharing at least oneattribute with the array, and with a substantially similar structure tothe proposed dataset, the results of such correlations ere being storedin an additional dataset; correlating the additional dataset with aproposed dataset, comprised of that includes proposed contentattributes; and reporting the results of such correlations as aprediction of future events.